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Knowledge-Based Neurocomputing: A Fuzzy Logic Approach: 234 (Studies in Fuzziness and Soft Computing, 234) - Softcover
Synopsis
This book details the state-of-the-art in knowledge-based neurocomputing. It introduces a novel fuzzy-rule base known as Fuzzy All-permutations Rule-Base (FARB) and presents new connections between artificial neural networks and FARB.
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From the Back Cover
In this monograph, the authors introduce a novel fuzzy rule-base, referred to as the Fuzzy All-permutations Rule-Base (FARB). They show that inferring the FARB, using standard tools from fuzzy logic theory, yields an input-output map that is mathematically equivalent to that of an artificial neural network. Conversely, every standard artificial neural network has an equivalent FARB.
The FARB-ANN equivalence integrates the merits of symbolic fuzzy rule-bases and sub-symbolic artificial neural networks, and yields a new approach for knowledge-based neurocomputing in artificial neural networks.
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Knowledge-Based Neurocomputing: A Fuzzy Logic Approach (eng)
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Knowledge-Based Neurocomputing: A Fuzzy Logic Approach
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ISBN 10: 3642099858
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Presents the state of the art in knowledge-based neurocomputing Presents a new connection between artificial neural networks (ANNs) and a special fuzzy rule-base - the all permutations fuzzy rule-base (FARB)In this monograph, the author. Seller Inventory # 5048973
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Knowledge-Based Neurocomputing: A Fuzzy Logic Approach
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - In this monograph, the authors introduce a novel fuzzy rule-base, referred to as the Fuzzy All-permutations Rule-Base (FARB). They show that inferring the FARB, using standard tools from fuzzy logic theory, yields an input-output map that is mathematically equivalent to that of an artificial neural network. Conversely, every standard artificial neural network has an equivalent FARB. The FARB-ANN equivalence integrates the merits of symbolic fuzzy rule-bases and sub-symbolic artificial neural networks, and yields a new approach for knowledge-based neurocomputing in artificial neural networks. Seller Inventory # 9783642099854
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Knowledge-Based Neurocomputing: A Fuzzy Logic Approach
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ISBN 10: 3642099858
ISBN 13: 9783642099854
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -In this monograph, the authors introduce a novel fuzzy rule-base, referred to as the Fuzzy All-permutations Rule-Base (FARB). They show that inferring the FARB, using standard tools from fuzzy logic theory, yields an input-output map that is mathematically equivalent to that of an artificial neural network. Conversely, every standard artificial neural network has an equivalent FARB.The FARB-ANN equivalence integrates the merits of symbolic fuzzy rule-bases and sub-symbolic artificial neural networks, and yields a new approach for knowledge-based neurocomputing in artificial neural networks.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 116 pp. Englisch. Seller Inventory # 9783642099854
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Knowledge-Based Neurocomputing: A Fuzzy Logic Approach
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ISBN 10: 3642099858
ISBN 13: 9783642099854
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -In this monograph, the authors introduce a novel fuzzy rule-base, referred to as the Fuzzy All-permutations Rule-Base (FARB). They show that inferring the FARB, using standard tools from fuzzy logic theory, yields an input-output map that is mathematically equivalent to that of an artificial neural network. Conversely, every standard artificial neural network has an equivalent FARB. The FARB-ANN equivalence integrates the merits of symbolic fuzzy rule-bases and sub-symbolic artificial neural networks, and yields a new approach for knowledge-based neurocomputing in artificial neural networks. 116 pp. Englisch. Seller Inventory # 9783642099854
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Knowledge-Based Neurocomputing: A Fuzzy Logic Approach (Studies in Fuzziness and Soft Computing, 234)
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